Analogue circuit fault diagnosis neural network method based on particle swarm algorithm

Abstract

The invention discloses a neural network method for diagnosing analog circuit failures which is based on a particle swarm algorithm, and comprises the following steps: imposing an actuating signal to an analog circuit to be tested, measuring an actuating response signal in the testing nodes of the circuit, extracting the candidate signal of failure characteristics by implementing noise elimination and then wavelet packet transformation on the measured actuating response signal, extracting the failure characteristics information by further implementing orthogonal principal component analysis and normalization processing on the candidate signal of failure characteristics, and sending the failure characteristics information as samples to the neural network for implementing classification. The method adopts the particle swarm algorithm instead of a gradient descent method in traditional BP algorithms, thus leading the improved algorithm to be characterized in that the algorithm avoids the local minimum problem and has better generalization performance. The BP neural network method for diagnosing the analog circuit failures which is optimized on the basis of particle swarm can obviously reduce iteration times in the algorithm, improve the precision of network convergence, and improve diagnosis speed and precision.

Description

technical field [0001] The invention relates to an analog circuit fault diagnosis method, in particular to a particle swarm algorithm-based neural network method for analog circuit fault diagnosis. Background technique [0002] Since the study of analog circuit fault diagnosis technology began in the 1960s, many achievements have been made. Researchers have proposed many methods, among which the component parameter identification method requires more information for diagnosis, requires a specific mathematical model, and mathematical operations Time-consuming. The pattern recognition method does not need a mathematical model, but only needs to use specific operation rules to map the measurement space to the decision space, avoiding complicated mathematical operations, thus greatly shortening the time, and only needing limited fault information, it can determine the faults in the network. component failure, and the implementation is relatively convenient, and has a good pract...

AI Technical Summary

Problems solved by technology

However, the BP neural network also has the defects that it is easy to fall into the local minimum, the generalization ability is weak or poor, and the convergence speed of the network is slow.

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